--- tags: - espnet - audio - diarization language: noinfo datasets: - librimix license: cc-by-4.0 --- ## ESPnet2 DIAR model ### `espnet/YushiUeda_librimix_diar_enh_2_3_spk_lmf` This model was trained by YushiUeda using librimix recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout 4f0f9a2435549211ef670354d09eb45883441b2d pip install -e . cd egs2/librimix/diar_enh2 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/YushiUeda_librimix_diar_enh_2_3_spk_lmf ``` # RESULTS ## Environments - date: `Sat Mar 26 08:47:28 EDT 2022` - python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]` - espnet version: `espnet 0.10.7a1` - pytorch version: `pytorch 1.10.1+cu102` - Git hash: `4f0f9a2435549211ef670354d09eb45883441b2d` - Commit date: `Tue Mar 15 10:52:24 2022 -0400` ## .. config: conf/tuning/train_diar_enh_convtasnet_lmf_adapt.yaml |dataset|STOI|SAR|SDR|SIR|SI_SNR|DER| |---|---|---|---|---|---|---| |diarized_enhanced_test|0.7667|8.1685|6.6069|15.2114|5.4204|6.04| ## DIAR config
expand ``` config: conf/tuning/train_diar_enh_convtasnet_lmf_adapt.yaml print_config: false log_level: INFO dry_run: false iterator_type: chunk output_dir: exp/diar_enh_train_diar_enh_convtasnet_lmf_adapt ngpu: 1 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 4 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 38467 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 100 patience: 4 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - si_snr_loss - min keep_nbest_models: 1 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 4 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: - exp/diar_enh_train_diar_enh_convtasnet_lmf/valid.si_snr_loss.best.pth ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 4 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/diar_enh_stats_8k/train/speech_mix_shape - exp/diar_enh_stats_8k/train/spk_labels_shape - exp/diar_enh_stats_8k/train/speech_ref1_shape - exp/diar_enh_stats_8k/train/speech_ref2_shape - exp/diar_enh_stats_8k/train/speech_ref3_shape - exp/diar_enh_stats_8k/train/noise_ref1_shape valid_shape_file: - exp/diar_enh_stats_8k/valid/speech_mix_shape - exp/diar_enh_stats_8k/valid/spk_labels_shape - exp/diar_enh_stats_8k/valid/speech_ref1_shape - exp/diar_enh_stats_8k/valid/speech_ref2_shape - exp/diar_enh_stats_8k/valid/speech_ref3_shape - exp/diar_enh_stats_8k/valid/noise_ref1_shape batch_type: folded valid_batch_type: null fold_length: - 800 - 80000 - 80000 - 80000 - 80000 - 80000 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 24000 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train/wav.scp - speech_mix - sound - - dump/raw/train/espnet_rttm - spk_labels - rttm - - dump/raw/train/spk1.scp - speech_ref1 - sound - - dump/raw/train/spk2.scp - speech_ref2 - sound - - dump/raw/train/spk3.scp - speech_ref3 - sound - - dump/raw/train/noise1.scp - noise_ref1 - sound valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech_mix - sound - - dump/raw/dev/espnet_rttm - spk_labels - rttm - - dump/raw/dev/spk1.scp - speech_ref1 - sound - - dump/raw/dev/spk2.scp - speech_ref2 - sound - - dump/raw/dev/spk3.scp - speech_ref3 - sound - - dump/raw/dev/noise1.scp - noise_ref1 - sound allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.001 eps: 1.0e-07 weight_decay: 0 scheduler: reducelronplateau scheduler_conf: mode: min factor: 0.5 patience: 1 num_spk: 3 init: xavier_uniform model_conf: loss_type: si_snr diar_weight: 0.2 attractor_weight: 0.2 use_preprocessor: true criterions: - name: si_snr conf: eps: 1.0e-07 wrapper: pit2 wrapper_conf: weight: 1.0 independent_perm: true frontend: default frontend_conf: fs: 8k hop_length: 64 specaug: specaug specaug_conf: apply_time_warp: false apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: null normalize_conf: {} diar_encoder: transformer diar_encoder_conf: input_size: 208 input_layer: conv2d8 num_blocks: 4 linear_units: 512 dropout_rate: 0.1 output_size: 256 attention_heads: 4 attention_dropout_rate: 0.1 diar_decoder: linear diar_decoder_conf: {} label_aggregator: label_aggregator label_aggregator_conf: win_length: 256 hop_length: 64 attractor: rnn attractor_conf: unit: 256 layer: 1 dropout: 0.1 attractor_grad: true enh_encoder: conv enh_encoder_conf: channel: 512 kernel_size: 16 stride: 8 separator: tcn separator_conf: layer: 8 stack: 3 bottleneck_dim: 128 hidden_dim: 512 kernel: 3 causal: false norm_type: gLN mask_module: mask mask_module_conf: max_num_spk: 3 mask_nonlinear: relu input_dim: 512 bottleneck_dim: 128 enh_decoder: conv enh_decoder_conf: channel: 512 kernel_size: 16 stride: 8 required: - output_dir version: 0.10.7a1 distributed: true ```
### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```